Efficient and reliable matching of image patches is of vital importance to a variety of applications. For example, recognition of a store, such as a popular coffee shop, in a photograph by matching its logo with a sign in a ground image would allow for enhanced capabilities in various applications, such as satellite imagery applications. Emerging techniques aim to use matching of image patches to determine if an object, such as a logo, exists within an image. Further, these emerging techniques attempt to locate the region of the image where the object exists.
Unfortunately, techniques typically used for matching are inefficient and unreliable, falling far short of the quality necessary to be useful in many applications. For instance, techniques that utilize the brightness difference between images (e.g. sum-of-squared-differences) are overly sensitive to background noise, variations in image appearances attributable to three-dimensional appearance, illumination changes, color inconsistencies, clutter, and occlusions. Further, by way of example, local image patch-based similarity measures are similarly inefficient and unreliable. These techniques fail too often because, although they are more consistent than other techniques in circumstances involving changes to illumination and color, minuteness and/or absence of certain edge gradients in local regions causes the local measures to indicate a false-negative. Many applications miss a vital opportunity to advance functionality because of the inefficiency and unreliability of traditional image patch matching techniques.